Perestrelo, R.; Silva, C.; Fernandes, M.X.; Câmara, J.S. Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model. Foods2019, 8, 628.
Perestrelo, R.; Silva, C.; Fernandes, M.X.; Câmara, J.S. Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model. Foods 2019, 8, 628.
Terpenoids, including monoterpenoids (C10), norisoprenoids (C13) and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites which chemical structure is highly diverse. A quantitative structure-activity relationship (QSAR) model to predict the terpenoids toxicity and to evaluate the influences of their chemical structure, was developed in this study, by assessing the toxicity of 27 terpenoid standards using Gram-negative bioluminescent Vibrio fischeri, in real time. Under the test conditions, at concentration of 1 µM, the terpenoids showed a toxicity level lower than five %, with exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30 % at concentration of 50 to 100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene and linalool oxide. Regarding the functional group, the terpenoids toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. CODESSA software was employed to develop the QSAR models based on the correlation of terpenoids toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoids toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., max partial charge for a C atom [Zefirov's PC]) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with training and test coefficient correlation higher than 0.810 and 0.535, respectively, and square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are a suitable and a rapid tool to predict the terpenoids toxicity in a diversity of food products.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.